And like the iPhone, AI has the potential to usher in an entirely new era for service providers, acting as a powerful engine that drives them towards the goal of becoming truly digital service
providers. It will transform the way they operate, the services they offer and, most importantly, their relationships with customers.

But while it is AI that is making the headlines, it’s what powers it that is the unsung trend: big data.

Data has long been understood to be the source for developing insights about a business and its operations, customers, and prospects. However, the role it plays in enabling AI to convert these
insights into strategy is equally critical. This is because essentially, data acts both as a mirror of what an organization is doing, as well as the effect it is having on its customers. On one
hand, this includes how effectively and efficiently the organization is conducting its business and what changes are needed in order to improve performance. On the other, it reveals what
customers are buying, what they want to buy and where they are experiencing issues. But the challenge then lies in extracting fresh, high-quality data, and combining it into a format that allows
it to be used as a source for AI. A frequently drawn analogy is oil, which like data is costly and time-consuming to locate and extract, and significantly, must also be refined before its full
value can be realized.

AI-Powered Automation

AI-powered automation — the ability to process information in real time (or as close to it as possible) — lies at the heart of an organization’s digital transformation. For service providers,
such a capability is becoming an increasing necessity. In many cases, it is simply no longer feasible to resolve every issue via human interaction or intervention due to the speed, scale or
complexity of the data that needs to be observed, analyzed, and acted upon. Driven by AI-powered automation, machines can be imbued with the “intelligence” to understand the situation at hand,
assess a range of options based on available information, and then select the best action or response based on the probability of the best outcome.

Business-Context Driven Predictive Models

Another important facet of AI is its role in developing predictive capabilities. Predictive analysis depends on historical data and allows, for example, the automation of predicting consumer
behavior so it can be pre-mitigated with an action that improves the customer experience. For example, it could enable you to know when a customer is likely to churn and then trigger a special
promotion that would entice the customer to stay. But acquiring such capabilities requires systems that know how to apply in-depth industry knowledge to historical data sets, and then understand
the indicators that point to a particular outcome — usually an approaching problem. Then, if these indicators can be identified, documented, and transformed into predictive AI, the system can
proactively monitor for similar signals in real-time data streams. Other applications include the ability to trigger preventative action before an issue actually occurs and impacts the customer
experience, such as when a specific location is likely to experience network capacity problems.

A 360-degree View of the customer

"Get closer than ever to your customers. So close that you tell them what they need well before they realize it for themselves. — Steve Jobs, when
co-founder and CEO of Apple.